Solomon Philip is Shift Technology’s Head of Market Intelligence
An increasing number of improper payments, along with Fraud, Waste, and Abuse (FWA) in general, are critical issues facing health plans and health payer organizations. At the same time, the investigative resources required to ensure the integrity of health payments are being stretched thin. Rising costs and a tight labor market have drastically reduced the number of trained investigators working in the industry, creating significant bottlenecks. In this challenging environment, how do health plans address improper payments and meet regulatory requirements without access to a sizable payment integrity and Special Investigative Unit (SIU) team? The only answer is to arm investigative teams with powerful AI capabilities. These technologies allow health plans to increase efficiencies across their limited resources and focus efforts toward detecting improper payments.
The Issue at Hand
SIUs are a necessary piece of the complex health payer ecosystem. In fact, some jurisdictions even mandate a minimum SIU-to-member ratio. Take for example the 1:60K SIU member ratio required by NYS OMIG. Industry best practices suggest that a 1:100K SIU member ratio is sufficient. Yet, over the last 5-10 years, SIU teams have seen their staffing reduced, in some instances by up to 50%.
Conventional wisdom may suggest that health plans are justified in making this call, as their post-pay teams have struggled to show ROI. At the same time, SIU teams are a hub for valuable intel from internal and external sources such as state and local authorities, hotline tips, call centers, and even members themselves. Without these critical sources, the fraud detection machinery and feedback loop suffers. And as a result, the ability to offer competitive premiums becomes increasingly challenging in a high-cost environment.
As important, this is all happening in the midst of increasing health plan consolidation which creates a highly competitive market where cost becomes the primary differentiator. And as we are all well aware, cost concerns are aggravated when fraud increases. Both prepay and postpay improper payment detection capabilities must become more robust to combat the emerging schemes which are growing in sophistication and severity.
Optimizing Costs with AI
In this environment, it is clear that investing in tools and resources for both payment integrity and SIU teams makes sense. As previously mentioned, prepay capabilities can often stagnate without necessary investments in postpay resources, thus impairing the payment integrity team’s effectiveness. Diminished effectiveness and impact are the direct result of the lack of vital reinforcement learning derived from post-pay investigations.
Unfortunately, health plans looking to minimize costs and meet regulatory requirements often consider lean SIU teams a source of cost savings. Prepay investments in tools, technology, and people are viewed as vital in stopping fraud. However, these investments cannot be made at the expense of investing and nurturing postpay groups. Making people or AI technology investments in SIU enhances a health plan's overall fraud detection machinery. Health plans struggling to optimize costs must look to AI to help drive down costs in postpay while developing comprehensive fraud detection capabilities and taking their initiatives to the next level.
The Total Impact of the SIU
Showing immediate and significant ROI from SIU activities in health payer organizations has traditionally been difficult. Typically, health plans report experiencing an ROI of approximately 5-10 cents for every dollar spent. As a result, it is understandable that health plans looking to capture savings may pull resources from their existing SIU teams into prepay in an effort to reallocate assets in a manner believed to be more beneficial.
However, this way of thinking is incredibly shortsighted. ROI offered by SIU diminishes further without technology investments. Yet, the results of postpay investigations fuel prepay payment integrity efforts. Without these learnings, payment integrity effectiveness and accuracy diminishes, limiting the benefits of programs solely focused on payment integrity dependent on claims adjudication systems. Claim adjudication systems (CAS) supporting pre-pay payment integrity programs need a constant and consistent flow of real-time/immediate insight, intelligence, and feedback from post-pay teams/tools, which stagnate without investing resources and tools into SIU. Reliance on rudimentary rules-based pre-pay detection significantly increases the probability of missing the most complex fraud schemes and regulators can issue fines for missing fraud, increasing overall costs to health payers. Adding further pressure for fast, accurate pre-payment decisioning are “prompt-pay” laws, requiring SIU teams to release payments within stipulated time frames. These financial and reputational impacts on the health plans’ brand severely impairs its ability to land contracts with the government, providers, and corporations. Further, poor brand reputation inhibits the ability of health plans to enable providers to deliver better patient care and reduce provider abrasion.
A New Course of Action
Health plans looking at all claims in aggregation (both past and present) for networks and connections increase their chances of catching fraud patterns, outliers, and oddities missed when investigating one case at a time, which can cost payers millions. Instead of managing cases using folders, static documents, and spreadsheets, health plans must implement case workflow solutions that increase real-time collaboration, track all claims activities and updates, as well as reduce redundancies or overlooked tasks.
Creating a feedback mechanism leveraging reinforcement learning from post-pay outcomes is also fundamental to a robust fraud detection strategy. Learnings must include insights from internal investigative analysis and tips from external sources. Providing such is an impossibility for a claims auditor or investigator who is only looking for simple flags to accumulate and assess across a large body of complex claims. On average, typical claims cycle time can take two weeks and even months, based on its complexity. Health plans should build an efficient SIU Center of Excellence with the tools, techniques, and technologies required for efficiency and accuracy,while using AI to automate existing tasks and deploying human resources to investigate complex and higher-value cases from new and emerging schemes. Industry reports indicate that using AI/ML can reduce typical claims cycle times by 50%.
AI’s Role
So, how exactly does AI fit into the improper payment and fraud detection strategies of forward-thinking health payers? Fundamentally, health plans can significantly reduce this investigative process by using AI to replicate the organization’s “best SIU investigator” at scale while enhancing the quality and leveraging prescriptive and proactive insights into suspect providers, claims, and member behavior. Optimizing SIU resources with AI can further assist health plans in bringing down the SIU-to-member ratio (approximately 1:120-240K) and amplify the impact of each investigator. AI can learn from alerts and case outcomes and analyze medical records and images to detect inconsistencies and anomalies. AI offers detailed, real-time explanations for each alert which reduces time and redundancies in the investigative process and which a rules-based engine simply cannot do.
Online collaboration amongst team members is also critical to accelerating claims investigation, payment, or denial. Health plans must continue to use existing internal/external data, and add more sources, such as online maps, and even publicly available reviews and social media, to enhance the detection accuracy of AI models. Health insurers can leverage NLP on social media or provider review platforms for sentiment analysis, and document analysis to assist in detecting complex schemes, suspicious relationships, and networks or crime rings otherwise missed by humans or rules engines. Advanced AI in SIU continually allows the prepay workflow process to evolve through reinforcement learning from case outcomes. Using the right AI in post-pay will enable health plans to apply learnings to pre-pay models, thus avoiding losses, shortening investigation times, and reducing the amount of pay and chasing efforts for false positives.
Conclusion
Health plans can utilize AI to develop fraud detection capabilities in post-pay SIUs, and these learnings are fundamental to robust payment integrity teams. Health plans must continue looking at claims holistically, aggregating learnings gleaned from past and present data sources, This ability has proven incredibly difficult for a human or a rules engine to replicate consistently and at scale. Finally, converting SIU teams into Centers of Excellence with tools and technologies needed to automate the capture of existing and emerging fraud, and disseminating these abilities seamlessly across the life cycle of a claim is one of the most effective means of realizing immediate ROI for health plans.
Many thanks to Shift's healthcare team for their assistance in developing this post.
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